Curb your violence, officer

The Charlotte-Mecklenburg Police Department in North Carolina is piloting the system in an attempt to tackle the police violence that has become a heated issue in the US in the past three years. A team at the University of Chicago is helping them feed their data into a machine learning system that learns to spot risk factors for unprofessional conduct. The department can then step in before risk transforms into actual harm.
The idea is to prevent incidents in which officers who are stressed behave aggressively, for example, such as one in Texas where an officer pulled his gun on children at a pool party after responding to two suicide calls earlier that shift. Ideally, early warning systems would be able to identify individuals who had recently been deployed on tough assignments, and divert them from other sensitive calls.
The system being tested in Charlotte is designed to include all of the records a department holds on an individual – from details of previous misconduct and gun use to their deployment history, such as how many suicide or domestic violence calls they have responded to. It retrospectively caught 48 out of 83 adverse incidents between 2005 and now – 12 per cent more than Charlotte-Mecklenberg’s existing early intervention system.
More importantly, the false positive rate – the fraction of officers flagged as being under stress who do not go on to act aggressively – was 32 per cent lower than the existing system’s.